Feature-Based Image Clustering and Segmentation Using Wavelets
This work addresses image segmentation for computer vision applications, but it is incremental as it modifies existing algorithms with wavelet features.
The paper tackled the problem of image segmentation and clustering by incorporating wavelet features to address noise and lack of spatial context in pixel intensity-based methods, resulting in modified algorithms that converge to different segmentation results based on frequency information.
Pixel intensity is a widely used feature for clustering and segmentation algorithms, the resulting segmentation using only intensity values might suffer from noises and lack of spatial context information. Wavelet transform is often used for image denoising and classification. We proposed a novel method to incorporate Wavelet features in segmentation and clustering algorithms. The conventional K-means, Fuzzy c-means (FCM), and Active contour without edges (ACWE) algorithms were modified to adapt Wavelet features, leading to robust clustering/segmentation algorithms. A weighting parameter to control the weight of low-frequency sub-band information was also introduced. The new algorithms showed the capability to converge to different segmentation results based on the frequency information derived from the Wavelet sub-bands.